Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Fusing Neural Networks Through Space Partitioning and Fuzzy Integration
Neural Processing Letters
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent vocal cord image analysis for categorizing laryngeal diseases
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
Classifier combination based on confidence transformation
Pattern Recognition
WeAidU-a decision support system for myocardial perfusion images using artificial neural networks
Artificial Intelligence in Medicine
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.